Articles | Volume 16, issue 5
https://doi.org/10.5194/tc-16-2103-2022
https://doi.org/10.5194/tc-16-2103-2022
Research article
 | 
01 Jun 2022
Research article |  | 01 Jun 2022

Estimating a mean transport velocity in the marginal ice zone using ice–ocean prediction systems

Graig Sutherland, Victor de Aguiar, Lars-Robert Hole, Jean Rabault, Mohammed Dabboor, and Øyvind Breivik

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-289', Anonymous Referee #1, 05 Dec 2021
    • AC1: 'Reply on RC1', Graig Sutherland, 27 Jan 2022
  • RC2: 'Comment on tc-2021-289', Anonymous Referee #2, 08 Jan 2022
    • AC2: 'Reply on RC2', Graig Sutherland, 27 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (09 Feb 2022) by Christian Haas
AR by Graig Sutherland on behalf of the Authors (22 Feb 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (07 Mar 2022) by Christian Haas
RR by Anonymous Referee #1 (20 Mar 2022)
ED: Reconsider after major revisions (further review by editor and referees) (02 Apr 2022) by Christian Haas
AR by Graig Sutherland on behalf of the Authors (28 Apr 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 May 2022) by Christian Haas
AR by Graig Sutherland on behalf of the Authors (06 May 2022)
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Short summary
The marginal ice zone (MIZ), which is the transition region between the open ocean and the dense pack ice, is a very dynamic region comprising a mixture of ice and ocean conditions. Using novel drifters deployed in various ice conditions in the MIZ, several material transport models are tested with two operational ice–ocean prediction systems. A new general transport equation, which uses both the ice and ocean solutions, is developed that reduces the error in drift prediction for our case study.